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tests/test_filter_pydantic_sub_model_pv2.py
return name async def get_model_c() -> ModelC: return ModelC(username="test-user", password="test-password") @app.get("/model/{name}", response_model=ModelA) async def get_model_a(name: str, model_c=Depends(get_model_c)): return {"name": name, "description": "model-a-desc", "foo": model_c} client = TestClient(app) return client @needs_pydanticv2
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docs/pt/docs/tutorial/body-nested-models.md
## Modelos aninhados Cada atributo de um modelo Pydantic tem um tipo. Mas esse tipo pode ser outro modelo Pydantic. Portanto, você pode declarar "objects" JSON profundamente aninhados com nomes, tipos e validações de atributos específicos. Tudo isso, aninhado arbitrariamente. ### Defina um sub-modelo Por exemplo, nós podemos definir um modelo `Image`: ```Python hl_lines="9-11"
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docs/de/docs/tutorial/extra-models.md
Das ist nützlich, wenn Sie die gültigen Feld-/Attribut-Namen von vorneherein nicht wissen (was für ein Pydantic-Modell notwendig ist). In diesem Fall können Sie `typing.Dict` verwenden (oder nur `dict` in Python 3.9 und darüber): === "Python 3.9+" ```Python hl_lines="6" {!> ../../../docs_src/extra_models/tutorial005_py39.py!} ```
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docs/pt/docs/tutorial/extra-models.md
# Modelos Adicionais Continuando com o exemplo anterior, será comum ter mais de um modelo relacionado. Isso é especialmente o caso para modelos de usuários, porque: * O **modelo de entrada** precisa ser capaz de ter uma senha. * O **modelo de saída** não deve ter uma senha. * O **modelo de banco de dados** provavelmente precisaria ter uma senha criptografada. !!! danger
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docs/de/docs/tutorial/response-model.md
`response_model` nimmt denselben Typ entgegen, den Sie auch für ein Pydantic-Modellfeld deklarieren würden, also etwa ein Pydantic-Modell, aber es kann auch z. B. eine `list`e von Pydantic-Modellen sein, wie etwa `List[Item]`.
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docs/en/docs/tutorial/extra-models.md
# Extra Models Continuing with the previous example, it will be common to have more than one related model. This is especially the case for user models, because: * The **input model** needs to be able to have a password. * The **output model** should not have a password. * The **database model** would probably need to have a hashed password. !!! danger Never store user's plaintext passwords. Always store a "secure hash" that you can then verify.
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docs/ru/docs/tutorial/extra-models.md
{!> ../../../docs_src/extra_models/tutorial003_py310.py!} ``` === "Python 3.8+" ```Python hl_lines="1 14-15 18-20 33" {!> ../../../docs_src/extra_models/tutorial003.py!} ``` ### `Union` в Python 3.10 В этом примере мы передаём `Union[PlaneItem, CarItem]` в качестве значения аргумента `response_model`.
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docs/en/docs/tutorial/response-model.md
`response_model` receives the same type you would declare for a Pydantic model field, so, it can be a Pydantic model, but it can also be, e.g. a `list` of Pydantic models, like `List[Item]`. FastAPI will use this `response_model` to do all the data documentation, validation, etc. and also to **convert and filter the output data** to its type declaration. !!! tip
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docs/de/docs/tutorial/body-nested-models.md
=== "Python 3.10+" ```Python hl_lines="12" {!> ../../../docs_src/body_nested_models/tutorial002_py310.py!} ``` === "Python 3.9+" ```Python hl_lines="14" {!> ../../../docs_src/body_nested_models/tutorial002_py39.py!} ``` === "Python 3.8+" ```Python hl_lines="14" {!> ../../../docs_src/body_nested_models/tutorial002.py!} ``` ## Set-Typen
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docs/en/docs/tutorial/body-nested-models.md
## Deeply nested models You can define arbitrarily deeply nested models: === "Python 3.10+" ```Python hl_lines="7 12 18 21 25" {!> ../../../docs_src/body_nested_models/tutorial007_py310.py!} ``` === "Python 3.9+" ```Python hl_lines="9 14 20 23 27" {!> ../../../docs_src/body_nested_models/tutorial007_py39.py!} ``` === "Python 3.8+"
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